TransGrid Energy
Context
TransGrid Energy, a Hanwha Group company, builds EnergyFluo, an agentic AI energy management platform for data centers and other large-load customers, announced publicly in September 2026. I have been embedded with the quality and reliability engineering team behind the platform since February 2026: the engagement started with Q CELLS North America, one of the largest solar manufacturers operating in the region, and moved to TransGrid Energy on October 1, 2026.
As described in that announcement, the product is a software intelligence layer over physical energy infrastructure. It reads on-site operating data, recommends actions and, once an operator approves, executes them according to the facility's standard operating procedures, staying within the mechanical limits of the equipment through physics-informed models. It is vendor and asset agnostic and runs at single-site or fleet level.
What I do
I joined as Quality and Delivery Operations Lead for the SRE and QA teams. Since August 2026 the role has also carried de facto product and program ownership of that team's release path, and since October 2026 support for the platform's transition from Q CELLS to TransGrid Energy. The engagement is ongoing.
- Designed the merge gate for the deployment repository (code-owner review, required validation, no bypass) and the promotion-gate model between environments.
- Built an SLO-gated promotion pilot on request-level error budgets, with observe and enforce modes.
- Ran incident root-cause analyses and the production-readiness and service-ownership audits that followed.
- Set up delivery reporting for leadership: DORA-style metrics and automated daily briefs generated from the delivery systems.
- Contributed tooling to the alarm pipeline: an industrial-protocol codec, a fleet simulator, and a shared alarm-event schema, merged into the platform repositories.
- Built a clickable product prototype used in leadership reviews, and authored the platform test plan across authentication, telemetry, digital-twin, and optimizer workflows.
- Lead the program that turns a new customer environment into a repeatable, documented deployment, and evaluated feature-flag guarded rollouts with automatic rollback for it.
What it is teaching me
Release controls are part of what makes an AI system dependable, alongside the model and the domain logic it encodes. The demo is the easy half. The promotion path, the contracts between services, and the runbooks are what let an organization run the system without the people who built it.